In PyCaret getting error:"ValueError: Cannot cast object dtype to float64" - streamlit

Question

While using PyCaret's ML capabilities I am facing the following error.
The ML Code I am using:
if choice == "ML":
st.title("Your Machine Learning Process Starts Here!")
target = st.selectbox("Select Your Target", df.columns)
setup(df, target=target, silent=True)
setup_df = pull()
st.info("This is the ML Experiment Settings")
st.dataframe(setup_df)
best_model = compare_models()
compare_df = pull()
st.info("This is the ML Model")
st.dataframe(compare_df)
best_model
Now while running the code I am getting the error:
TypeError: setup() got an unexpected keyword argument 'silent'
Traceback:
File "D:\Python Projects\Machine-Learning-App\mlapp\lib\site-packages\streamlit\runtime\scriptrunner\script_runner.py", line 563, in _run_script
exec(code, module.__dict__)
File "D:\Python Projects\Machine-Learning-App\app.py", line 37, in <module>
setup(df, target=target, silent=True)
Hence, I removed the silent=True and now am getting the following error:
ValueError: Cannot cast object dtype to float64
Traceback:
File "D:\Python Projects\Machine-Learning-App\mlapp\lib\site-packages\streamlit\runtime\scriptrunner\script_runner.py", line 563, in _run_script
exec(code, module.__dict__)
File "D:\Python Projects\Machine-Learning-App\app.py", line 37, in <module>
setup(df, target=target)
File "C:\Users\aviparna.biswas\AppData\Roaming\Python\Python39\site-packages\pycaret\classification\functional.py", line 596, in setup
return exp.setup(
File "C:\Users\aviparna.biswas\AppData\Roaming\Python\Python39\site-packages\pycaret\classification\oop.py", line 885, in setup
self.pipeline.fit(self.X_train, self.y_train)
File "C:\Users\aviparna.biswas\AppData\Roaming\Python\Python39\site-packages\pycaret\internal\pipeline.py", line 211, in fit
X, y, _ = self._fit(X, y, **fit_params_steps)
File "C:\Users\aviparna.biswas\AppData\Roaming\Python\Python39\site-packages\pycaret\internal\pipeline.py", line 192, in _fit
X, y, fitted_transformer = self._memory_fit(
File "D:\Python Projects\Machine-Learning-App\mlapp\lib\site-packages\joblib\memory.py", line 594, in __call__
return self._cached_call(args, kwargs)[0]
File "D:\Python Projects\Machine-Learning-App\mlapp\lib\site-packages\joblib\memory.py", line 537, in _cached_call
out, metadata = self.call(*args, **kwargs)
File "D:\Python Projects\Machine-Learning-App\mlapp\lib\site-packages\joblib\memory.py", line 779, in call
output = self.func(*args, **kwargs)
File "C:\Users\aviparna.biswas\AppData\Roaming\Python\Python39\site-packages\pycaret\internal\pipeline.py", line 87, in _fit_transform_one
_fit_one(transformer, X, y, message, **fit_params)
File "C:\Users\aviparna.biswas\AppData\Roaming\Python\Python39\site-packages\pycaret\internal\pipeline.py", line 54, in _fit_one
transformer.fit(*args, **fit_params)
File "C:\Users\aviparna.biswas\AppData\Roaming\Python\Python39\site-packages\pycaret\internal\preprocess\transformers.py", line 216, in fit
self.transformer.fit(*args, **fit_params)
File "D:\Python Projects\Machine-Learning-App\mlapp\lib\site-packages\sklearn\impute\_base.py", line 364, in fit
X = self._validate_input(X, in_fit=True)
File "D:\Python Projects\Machine-Learning-App\mlapp\lib\site-packages\sklearn\impute\_base.py", line 319, in _validate_input
raise ve
File "D:\Python Projects\Machine-Learning-App\mlapp\lib\site-packages\sklearn\impute\_base.py", line 302, in _validate_input
X = self._validate_data(
File "D:\Python Projects\Machine-Learning-App\mlapp\lib\site-packages\sklearn\base.py", line 577, in _validate_data
X = check_array(X, input_name="X", **check_params)
File "D:\Python Projects\Machine-Learning-App\mlapp\lib\site-packages\sklearn\utils\validation.py", line 791, in check_array
array = array.astype(new_dtype)
File "D:\Python Projects\Machine-Learning-App\mlapp\lib\site-packages\pandas\core\generic.py", line 5912, in astype
new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)
File "D:\Python Projects\Machine-Learning-App\mlapp\lib\site-packages\pandas\core\internals\managers.py", line 419, in astype
return self.apply("astype", dtype=dtype, copy=copy, errors=errors)
File "D:\Python Projects\Machine-Learning-App\mlapp\lib\site-packages\pandas\core\internals\managers.py", line 304, in apply
applied = getattr(b, f)(**kwargs)
File "D:\Python Projects\Machine-Learning-App\mlapp\lib\site-packages\pandas\core\internals\blocks.py", line 580, in astype
new_values = astype_array_safe(values, dtype, copy=copy, errors=errors)
File "D:\Python Projects\Machine-Learning-App\mlapp\lib\site-packages\pandas\core\dtypes\cast.py", line 1292, in astype_array_safe
new_values = astype_array(values, dtype, copy=copy)
File "D:\Python Projects\Machine-Learning-App\mlapp\lib\site-packages\pandas\core\dtypes\cast.py", line 1234, in astype_array
values = values.astype(dtype, copy=copy)
File "D:\Python Projects\Machine-Learning-App\mlapp\lib\site-packages\pandas\core\arrays\categorical.py", line 556, in astype
raise ValueError(msg)
Is there any workaround this error?
For installing pycaret I have used the command pip install -U --pre pycaret the latest release 3.0.0rc2 which works in python 3.9.

Answer 1

I had the same issue. It is working for me when I use python 3.7.9, but not 3.8.x or 3.9.x (as of now).

Answer 2

The 3.0.0rc2 version does not support silent parameter in setup() anymore. Hence the first error.
About this line:
setup(df, target=target)
The error would indicate that your labels are not of numerical type but an object. I'd look into that.

Answer 3

This is due to Pycaret not being compatible with scikit-learn version.
As the documentation says (https://pycaret.gitbook.io/docs/get-started/installation)
"PyCaret is not yet compatible with sklearn>=0.23.2"
Try downgrading scikit-learn:
pip install scikit-learn==0.23.2